KarrasVeScheduler
译者:片刻小哥哥
项目地址:https://huggingface.apachecn.org/docs/diffusers/api/schedulers/stochastic_karras_ve
原始地址:https://huggingface.co/docs/diffusers/api/schedulers/stochastic_karras_ve
KarrasVeScheduler
is a stochastic sampler tailored o variance-expanding (VE) models. It is based on the
Elucidating the Design Space of Diffusion-Based Generative Models
and
Score-based generative modeling through stochastic differential equations
papers.
KarrasVeScheduler
class
diffusers.
KarrasVeScheduler
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_karras_ve.py#L49)
(
sigma_min
: float = 0.02
sigma_max
: float = 100
s_noise
: float = 1.007
s_churn
: float = 80
s_min
: float = 0.05
s_max
: float = 50
)
Parameters
- sigma_min
(
float
, defaults to 0.02) — The minimum noise magnitude. - sigma_max
(
float
, defaults to 100) — The maximum noise magnitude. - s_noise
(
float
, defaults to 1.007) — The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000, 1.011]. - s_churn
(
float
, defaults to 80) — The parameter controlling the overall amount of stochasticity. A reasonable range is [0, 100]. - s_min
(
float
, defaults to 0.05) — The start value of the sigma range to add noise (enable stochasticity). A reasonable range is [0, 10]. - s_max
(
float
, defaults to 50) — The end value of the sigma range to add noise. A reasonable range is [0.2, 80].
A stochastic scheduler tailored to variance-expanding models.
This model inherits from SchedulerMixin and ConfigMixin . Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving.
For more details on the parameters, see
Appendix E
. The grid search values used
to find the optimal
{s_noise, s_churn, s_min, s_max}
for a specific model are described in Table 5 of the paper.
add_noise_to_input
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_karras_ve.py#L138)
(
sample
: FloatTensor
sigma
: float
generator
: typing.Optional[torch._C.Generator] = None
)
Parameters
- sample
(
torch.FloatTensor
) — The input sample. - sigma
(
float
) — - generator
(
torch.Generator
, optional ) — A random number generator.
Explicit Langevin-like “churn” step of adding noise to the sample according to a
gamma_i ≥ 0
to reach a
higher noise level
sigma_hat = sigma_i + gamma_i*sigma_i
.
scale_model_input
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_karras_ve.py#L99)
(
sample
: FloatTensor
timestep
: typing.Optional[int] = None
)
→
export const metadata = 'undefined';
torch.FloatTensor
Parameters
- sample
(
torch.FloatTensor
) — The input sample. - timestep
(
int
, optional ) — The current timestep in the diffusion chain.
Returns
export const metadata = 'undefined';
torch.FloatTensor
export const metadata = 'undefined';
A scaled input sample.
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.
set_timesteps
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_karras_ve.py#L116)
(
num_inference_steps
: int
device
: typing.Union[str, torch.device] = None
)
Parameters
- num_inference_steps
(
int
) — The number of diffusion steps used when generating samples with a pre-trained model. - device
(
str
ortorch.device
, optional ) — The device to which the timesteps should be moved to. IfNone
, the timesteps are not moved.
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
step
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_karras_ve.py#L164)
(
model_output
: FloatTensor
sigma_hat
: float
sigma_prev
: float
sample_hat
: FloatTensor
return_dict
: bool = True
)
→
export const metadata = 'undefined';
~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput
or
tuple
Parameters
- model_output
(
torch.FloatTensor
) — The direct output from learned diffusion model. - sigma_hat
(
float
) — - sigma_prev
(
float
) — - sample_hat
(
torch.FloatTensor
) — - return_dict
(
bool
, optional , defaults toTrue
) — Whether or not to return a~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput
ortuple
.
Returns
export const metadata = 'undefined';
~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput
or
tuple
export const metadata = 'undefined';
If return_dict is
True
,
~schedulers.scheduling_karras_ve.KarrasVESchedulerOutput
is returned,
otherwise a tuple is returned where the first element is the sample tensor.
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise).
step_correct
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_karras_ve.py#L203)
(
model_output
: FloatTensor
sigma_hat
: float
sigma_prev
: float
sample_hat
: FloatTensor
sample_prev
: FloatTensor
derivative
: FloatTensor
return_dict
: bool = True
)
→
export const metadata = 'undefined';
prev_sample (TODO)
Parameters
- model_output
(
torch.FloatTensor
) — The direct output from learned diffusion model. - sigma_hat
(
float
) — TODO - sigma_prev
(
float
) — TODO - sample_hat
(
torch.FloatTensor
) — TODO - sample_prev
(
torch.FloatTensor
) — TODO - derivative
(
torch.FloatTensor
) — TODO - return_dict
(
bool
, optional , defaults toTrue
) — Whether or not to return a DDPMSchedulerOutput ortuple
.
Returns
export const metadata = 'undefined';
prev_sample (TODO)
export const metadata = 'undefined';
updated sample in the diffusion chain. derivative (TODO): TODO
Corrects the predicted sample based on the
model_output
of the network.
KarrasVeOutput
class
diffusers.schedulers.scheduling_karras_ve.
KarrasVeOutput
[<
source
](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_karras_ve.py#L29)
(
prev_sample
: FloatTensor
derivative
: FloatTensor
pred_original_sample
: typing.Optional[torch.FloatTensor] = None
)
Parameters
- prev_sample
(
torch.FloatTensor
of shape(batch_size, num_channels, height, width)
for images) — Computed sample (x_{t-1}) of previous timestep.prev_sample
should be used as next model input in the denoising loop. - derivative
(
torch.FloatTensor
of shape(batch_size, num_channels, height, width)
for images) — Derivative of predicted original image sample (x_0). - pred_original_sample
(
torch.FloatTensor
of shape(batch_size, num_channels, height, width)
for images) — The predicted denoised sample (x_{0}) based on the model output from the current timestep.pred_original_sample
can be used to preview progress or for guidance.
Output class for the scheduler’s step function output.